Summary This proposal seeks to address fundamental methodological challenges associated with the development and use of multi-scale models (MSMs), and by extension, can potentially address a current epistemic crisis affecting biomedical research as a whole. We propose an approach by which a novel perspective of using MSMs, and specifically agent-based models (ABMs), provides a means of explaining and eventually addressing the Crisis of Reproducibility, and, in so doing, providing a tractable path towards ?real? Precision Medicine (i.e. right drug, right patient, right time, and how to design such a strategy). We assert that the Crisis of Reproducibility arises in great part because of the sparseness of ?real world? data relative to the space of all possible biological/pathological phenotypes (in terms of system state and especially system trajectories); this leads to a discordance between what can be sampled experimentally and the true richness of biological heterogeneity. We further propose that addressing this discrepancy can be accomplished by approximating the behavioral landscape of a system using large-scale parameter/trajectory space exploration of ABMs as proxies for the real world system. This perspective is novel because it emphasizes the distribution and variability of multi-dimensional spaces/manifolds generated by many trajectories, as opposed to the individual or highly- selected subset of trajectories that result from classical parameter fitting/calibration. Thus, the validation target shifts away from high-fidelity/precision fitting (e.g. fitting mean values of a single dataset), which contributes to the sparseness problem; instead, validation involves recapitulating the breadth of coverage and distribution of outcomes across many datasets, which embraces heterogeneity. Given the importance of system dynamics and the non-uniqueness of trajectories to a particular state, this perspective leads to our assertions that true Precision Medicine can only be achieved after behavioral manifolds are thoroughly characterized, and that, without an existing mathematical formalism, establishing the direction for developing control strategies can best be achieved using evolutionary computing and reinforcement learning on simulation data.